in my case perform some random operations on image lick flip, roll, move-up and after this action image set as it is how can I validate action is happening
If the actions has certain outputs like rotating image 90 degree, you can use visual regression for this.
Generate 90 degree rotated image, compare this with test's output. I know this is not a best practice.
Maybe you choose different way, I recommend you to develop another service which is compare two different images and return result using socket connection. This comparement process is easy in python.
Related
I'm using ITK-SNAP to compare the intensities of several Regions of Interest between several conditions.
For some subjects, I need to realign one image to another by using the Registration tool.
However, I noticed that the intensity values of a specific segmentation that I drew on the reference image doesn't change no matter how I register.
The value will be different between the two images, but even if I manually register the second image to something completely off, it will stay the same.
Is it possible to get the actual mean intensity of my segmentation depending on where it is on the registered image ?
Segmentation menu, option "Volumes and Statistics..." should show you what you are looking for.
Registration does not impact the intensity. Depending on how you transform your image, it affects the location and coordination of your voxels! It does not play with the intensities! It may reform, or reshape, rotate, or translate the image. If you expect different intensities after registration, you need to apply some other techniques rather than registration! because all the transformation matrix are applied on the coordination and location. You should play with the other features of your data!
There are some registration methods which influence the intensities but they are not used in ITKSNAP for example. You should look for its special package.
For example this paper is on:
Intensity based image registration by minimizing the complexity of weighted subtraction under illumination changes
Which is specifically playing with the intensities for fusion.
https://www.sciencedirect.com/science/article/abs/pii/S1746809415001755
Other example is this matlab script for Intensity based automatic registration, The process begins with the transform type you specify and an internally determined transformation matrix. Together, they determine the specific image transformation that is applied to the moving image with bilinear interpolation.
https://www.sciencedirect.com/science/article/abs/pii/S1746809415001755
I'm working on a game (using Game Maker: Studio Professional v1.99.355) that needs to have both user-modifiable level geometry and AI pathfinding based on platformer physics. Because of this, I need a way to dynamically figure out which platforms can be reached from which other platforms in order to build a node graph I can feed to A*.
My current approach is, more or less, this:
For each platform consider each other platform in the level.
For each of those platforms, if it is obviously unreachable (due to being higher than the maximum jump height, for example) do not form a link and move on to next platform.
If a link seems possible, place an ai_character instance on the starting platform and (within the current step event) simulate a jump attempt.
3.a Repeat this jump attempt for each possible starting position on the starting platform.
If this attempt is successful, record the data necessary to replicate it in real time and move on to the next platform.
If not, do not form a link.
Repeat for all platforms.
This approach works, more or less, and produces a link structure that when visualised looks like this:
linked platforms (Hyperlink because no rep.)
In this example the mostly-concealed pink ghost in the lower right corner is trying to reach the black and white box. The light blue rectangles are just there to highlight where recognised platforms are, the actual platforms are the rows of grey boxes. Link lines are green at the origin and red at the destination.
The huge, glaring problem with this approach is that for a level of only 17 platforms (as shown above) it takes over a second to generate the node graph. The reason for this is obvious, the yellow text in the screen centre shows us how long it took to build the graph: over 24,000(!) simulated frames, each with attendant collision checks against every block - I literally just run the character's step event in a while loop so everything it would normally do to handle platformer movement in a frame it now does 24,000 times.
This is, clearly, unacceptable. If it scales this badly at a mere 17 platforms then it'll be a joke at the hundreds I need to support. Heck, at this geometric time cost it might take years.
In an effort to speed things up, I've focused on the other important debugging number, the tests counter: 239. If I simply tried every possible combination of starting and destination platforms, I would need to run 17 * 16 = 272 tests. By figuring out various ways to predict whether a jump is impossible I have managed to lower the number of expensive tests run by a whopping 33 (12%!). However the more exceptions and special cases I add to the code the more convinced I am that the actual problem is in the jump simulation code, which brings me at long last to my question:
How would you determine, with complete reliability, whether it is possible for a character to jump from one platform to another, preferably without needing to simulate the whole jump?
My specific platform physics:
Jumps are fixed height, unless you hit a ceiling.
Horizontal movement has no acceleration or inertia.
Horizontal air control is allowed.
Further info:
I found this video, which describes a similar problem but which doesn't provide a good solution. This is literally the only resource I've found.
You could limit the amount of comparisons by only comparing nearby platforms. I would probably only check the horizontal distance between platforms, and if it is wider than the longest jump possible, then don't bother checking for a link between those two. But you might have done this since you checked for the max height of a jump.
I glanced at the video and it gave me an idea. Instead of looking at all platforms to find which jumps are impossible, what if you did the opposite? Try placing an AI character on all platforms and see which other platforms they can reach. That's certainly easier to implement if your enemies can't change direction in midair though. Oh well, brainstorming is the key to finding something.
Several ideas you could try out:
Limit the amount of comparisons you need to make by using a spatial data structure, like a quad tree. This would allow you to severely limit how many platforms you're even trying to check. This is mostly the same as what you're currently doing, but a bit more generic.
Try to pre-compute some jump trajectories ahead of time. This will not catch all use cases that you have - as you allow for full horizontal control - but might allow you to catch some common cases more quickly
Consider some kind of walkability grid instead of a link generation scheme. When geometry is modified, compute which parts of the level are walkable and which are not, with some resolution (something similar to the dimensions of your agent might be good starting point). You could also filter them with a height, so that grid tiles that are higher than your jump height, and you can't drop from a higher place on to them, are marked as unwalkable. Then, when you compute your pathfinding, as part of your pathfinding step you can compute when you start a jump, if a path is actually executable ('start a jump, I can go vertically no more than 5 tiles, and after the peak of the jump, i always fall down vertically with some speed).
I am working on a structure from motion application and I am tracking a number of markers placed on the object to determine the rigid structure of the object.
The app is essentially using standard Levenberg-Marquardt optimization over multiple camera views and minimizing the differences between expected marker points and the marker points obtained in 2D from each view.
For each marker point and each view the following function is minimised:
double diff = calculatedXY[index] - observedXY[index]
Where calculatedXY value depends on a number of unknown parameters that need to be found via the optimization and observedXY is the marker point position in 2D. In total I have (marker points * views) number of functions like the one above that I am aiming to minimise.
I have coded up a simulation of the camera seeing all the marker points but I was wondering how to handle the cases when during running the points are not visible due to lighting, occlusion or just not being in the camera view. In the real running of the app I will be using a web cam to view the object so it is likely that not all markers will be visible at once and depending on how robust my computer vision algorithm is, I might not be able to detect a marker all the time.
I thought of setting the diff value to be 0 (sigma squared difference = 0) in the case where the marker point could not be observed, could this skew the results however?
Another thing I noticed is that the algorithm is not as good when presented with too many views. It is more likely to estimate a bad solution when presented with too many views. Is this a common problem with bundle adjustment due to the increased likeliness of hitting a local minimum when presented with too many views?
It is common practice to just leave out terms corresponding to missing markers. Ie. don't try to minimise calculateXY-observedXY if there is no observedXY term. There's no need to set anything to zero, you shouldn't even be considering this term in the first place - just skip it (or, I guess in your code, it's equivalent to set the error to zero).
Bundle adjustment can fail terribly if you simply throw a large number of observations at it. Build your solution up incrementally by solving with a few views first and then keep on adding.
You might want to try some kind of 'robust' approach. Instead of using least squares, use a "loss function"1. These allow your optimisation to survive even if there are a handful of observations that are incorrect. You can still do this in a Levenberg-Marquardt framework, you just need to incorporate the derivative of your loss function into the Jacobian.
I try to found the best method to do this, considering a turn by turn cross-plateform game on mobile (3G bandwidth) with projectile and falling blocks.
I wonder if one device (the current player turn = server role) can run the physics and send some "key frames" data (position, orientation of blocks) to the other device, which just interpolate from the current state to the "keyframes" received.
With this method I'm quite afraid about the huge amount of data to guarantee the same visual on the other player's device.
Another method should be to send the physics data (force, acceleration ...) and run physics on the other device too, but I'm afraid to never have the same result at all.
My current implementation works like this:
Server manages physics simulation
On any major collision of any object, the object's absolute position, rotation, AND velocity/acceleration/forces are sent to each client.
Client sets each object at the position along with their velocity and applies the necessary forces.
Client calculates latency and advances the physics system to accommodate for the lag time by that amount.
This, for me, works quite well. I have the physics system running over dozens of sub-systems (maps).
Some key things about my implementation:
Completely ignore any object that isn't flagged as "necessary". For instance, dirt and dust particles that respond to player movement or grass and water as it responds to player movement. Basically non-essential stuff.
All of this is sent through UDP by the way. This would be horrendous on TCP.
You will want to send absolute positions and rotations.
You're right, that if you send just forces, it won't work. It's possible to make this work, but it's much harder than just sending positions. You need both devices to do their calculations the same way, so before each frame, you need to wait for the input from the other device, you need to use the same time step, scripts need to either run in the same order or be commutative, and you can only use CPU instructions guaranteed to give the same result on both machines.
that last one is one that makes it particularly problematic, because it means you can't use floating-point numbers (floats/singles, or doubles). you have to use integers, or roll your own number format, so you can't take advantage of many existing tools.
Many games use a client-server model with client-side prediction. if your game is turn based, you might be able to get away with not using client-side prediction. instead, you could have the client lag behind by some amount of time, so that you can be fairly sure that the server's input will already be there when you go to render. client-side prediction is only important if the client can make changes that the server cares about (such as moving).
Flex has the SoundMixer.computeSpectrum function that lets you compute an FFT from the currently playing sound. What I'd like to do is compute an FFT without playing the sound. Since Flash 10.1 lets us access the microphone bytes directly, it seems like we should be able to compute the FFT directly off of what the user is speaking.
Unfortunately this doesn't work as far as I know. As stated on the Adobe help pages:
The SoundMixer.computeSpectrum()
method lets an application read the
raw sound data for the waveform that
is currently being played. If more
than one SoundChannel object is
currently playing the
SoundMixer.computeSpectrum() method
shows the combined sound data of every
SoundChannel object mixed together.
This implies two drawbacks:
It just works on the output (SoundChannel)
It just works on the mix of all outputs.
If you don't need the output channel at all, you may turn down it's volume to zero or near to zero!? Don't know if that could work.
For myself I don't see any other chance at the moment to implement the FFT on my own to compute a spectrum on the microphone data.
I'm not sure if there's a way to pass that data, but if all else fails, you can always compute the FFT yourself.